11 accepted papers
Large language models (LLMs) often generate hallucinations—fluent yet factually incorrect responses—that undermine reliability in knowledge-intensive tasks. Existing approaches for hallucination mitigation typically rely on external retrieval modules or probability heuristics, which either require a
The rapid progress of large language models (LLMs) has transformed natural language processing, yet the challenge of efficient adaptation remains unresolved. Full fine-tuning achieves strong performance but imposes prohibitive computational and memory costs. Parameter-efficient fine-tuning (PEFT) st…
In adaptive cruise control (ACC), balancing safety, comfort, and sustainability still remains challenging. Accordingly, we propose a hybrid reinforcement learning framework combining proximal policy optimization (PPO) and deep Q-network (DQN) with a multi-objective reward for autonomous carbon-neutr
End-to-end (E2E) autonomous driving must maintain global consistency while preserving local precision. However, existing E2E approaches rarely achieve both goals simultaneously. Therefore, we propose a multimodal coarse-to-local transformer (MC2L-Transformer), which is composed of a hierarchical tra
Robust reinforcement learning (RRL) aims to tackle unexpected environmental changes by optimizing policies against the worst case. However, RRL remains impractical due to the cost of the Max-Min optimization, where it suffers from the exhaustive query complexity for finding the worst-case (dubbed 'M…
Quantum machine learning (QML) has attracted growing interest for their ability to achieve superior performance with significantly fewer parameters. However, the high cost and scarcity of current hardware push inference to cloud-hosted quantum devices, creating a tension between verifiability and co
This paper proposes a novel quantum style transfer (QST) in hybrid quantum-classical computing. QST leverages quantum computing's ability to process high-dimensional data efficiently. Our approach aims to decrease both inference time and complexity while maintaining performance, presenting a viable…
Reinforcement learning (RL) using conventional neural networks (NN) has significantly progressed in various applications. However, conventional RL needs help training in environments with large-scale action dimensions, such as coordinated mobility/satellite systems. Quantum reinforcement learning (Q…
Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to…
Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing. Motivated by this, in this article we re-design multi-agent reinforcement learning (MARL) based…
This paper proposes a reliable and self-adaptive operating system management policy for CCTV-based security applications which controls arrival image compression rates. After receiving image sequences via CCTV cameras, the system enqueues the sequences of images and processes them for face recogniti…